{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-03-28T03:30:04.588955Z",
     "start_time": "2025-03-28T03:30:04.347239Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-28T03:30:20.681890Z",
     "start_time": "2025-03-28T03:30:04.601971Z"
    }
   },
   "cell_type": "code",
   "source": [
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "X, y = mnist.data, mnist.target.astype(np.int8)"
   ],
   "id": "8917a43106d85c15",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-28T03:30:22.003820Z",
     "start_time": "2025-03-28T03:30:21.281987Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ],
   "id": "a6cf2e885d42c285",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-28T03:30:22.881339Z",
     "start_time": "2025-03-28T03:30:22.030410Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
   ],
   "id": "30e0e57116ec47f6",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-28T03:30:28.877387Z",
     "start_time": "2025-03-28T03:30:22.909857Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "# Logistic回归模型训练\n",
    "model = LogisticRegression(max_iter=1000)\n",
    "model.fit(X_train, y_train)"
   ],
   "id": "15219749a8341924",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ],
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-28T03:30:28.929726Z",
     "start_time": "2025-03-28T03:30:28.908875Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 模型预测\n",
    "y_pred = model.predict(X_test)"
   ],
   "id": "1eff4e2ad155f07f",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-28T03:30:28.985462Z",
     "start_time": "2025-03-28T03:30:28.956578Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 模型评估\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1-score:\", f1)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)"
   ],
   "id": "ec65c2b8e47874f5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9155714285714286\n",
      "Precision: 0.9153404763625332\n",
      "Recall: 0.9155714285714286\n",
      "F1-score: 0.9153678291640769\n",
      "Confusion Matrix:\n",
      " [[1283    1   10    0    1   14   22    4    6    2]\n",
      " [   0 1553    6    9    3    5    1    4   16    3]\n",
      " [   5   19 1234   18   13   14   21   16   28   12]\n",
      " [   7    9   38 1271    1   40    7   21   21   18]\n",
      " [   6    2   11    4 1189    5   12    9    9   48]\n",
      " [  11   11    9   42   12 1116   20    2   35   15]\n",
      " [   6    4   18    1   16   21 1321    3    6    0]\n",
      " [   4    4   24    4   13    6    0 1413    2   33]\n",
      " [  11   32   17   44    6   46   13    9 1160   19]\n",
      " [   7    9    7   15   38    5    0   49   12 1278]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.96      0.96      1343\n",
      "           1       0.94      0.97      0.96      1600\n",
      "           2       0.90      0.89      0.90      1380\n",
      "           3       0.90      0.89      0.89      1433\n",
      "           4       0.92      0.92      0.92      1295\n",
      "           5       0.88      0.88      0.88      1273\n",
      "           6       0.93      0.95      0.94      1396\n",
      "           7       0.92      0.94      0.93      1503\n",
      "           8       0.90      0.85      0.87      1357\n",
      "           9       0.89      0.90      0.90      1420\n",
      "\n",
      "    accuracy                           0.92     14000\n",
      "   macro avg       0.91      0.91      0.91     14000\n",
      "weighted avg       0.92      0.92      0.92     14000\n",
      "\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-28T03:30:29.380222Z",
     "start_time": "2025-03-28T03:30:29.051238Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 使用热度图展示混淆矩阵，体现每个类别的识别效果\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title(\"Logistic Regression - Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "id": "dbd95b59b912a952",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-28T03:30:29.414285Z",
     "start_time": "2025-03-28T03:30:29.411839Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "146e98b75f50491c",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-28T03:30:29.450364Z",
     "start_time": "2025-03-28T03:30:29.448273Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "31d44aa7504172ee",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-28T03:30:29.484366Z",
     "start_time": "2025-03-28T03:30:29.482348Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "2ce921a04d7384f9",
   "outputs": [],
   "execution_count": null
  }
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 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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